I recently re-read the concept of “ten times better” and feel that it is even more important in the AI era.

Peter Thiel said in "From 0 to 1" that for a new solution to truly break the inertia, it is best to be 10 times better than the original solution.

Because there is a cost for user switching, three times better is often not enough.

Dan Sullivan later added another layer: Pursuing 10x is sometimes easier than pursuing 2x, because 2x will let you optimize along the old path, while 10x will force you to abandon most of the old path and re-select the part that is truly leveraged.

This judgment is very interesting when applied to AI.

Many people use AI, but in fact it is still stuck at 2x: do old work faster.

But what AI may really bring about is redefining the structure of work.

I have written all my recent thoughts and some practices into this article.

The content is a bit long and can be read in multiple times, haha.

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